Journal of Advanced Research in Civil and Environmental Engineering

Predicting Construction Quality Factors Using Artificial Neural Networks: An Analysis of Key Influences in Birendranagar, Surkhet, Nepal

2025-03-01T13:15:22+00:00

This study examines the key factors influencing construction quality in Birendranagar, Surkhet, focusing on manpower, equipment, management, funding, and duration-related challenges. A questionnaire survey was conducted with 59 respondents, and data were analyzed using the Relative Importance Index (RII) and Mean Value Response (MVR). The findings reveal that insufficient worker training (RII = 0.78), weak site supervision (RII = 0.81), outdated machinery with poor maintenance (RII = 0.82), and financial constraints (RII = 0.84) significantly impact construction quality. Additionally, poor planning and scheduling (RII = 0.83) contribute to delays and inefficiencies.

An Artificial Neural Network (ANN) model using IBM SPSS 27 was employed to predict the most influential factors affecting construction quality. While RII rankings identified funding-related factors as the most critical, ANN analysis indicated that machine efficiency, management, and project duration have a greater impact, highlighting a gap between stakeholder perceptions and data-driven insights. Sensitivity analysis further revealed that machine-related factors hold the highest importance (100%), followed by management (96%) and duration (85%), while manpower factors ranked lowest (68%).

The study emphasizes the need for workforce training, modern equipment maintenance, financial stability, and improved site management to enhance construction quality. A balanced approach that integrates perception-based insights with data-driven findings is recommended for effective decision-making and sustainable improvements in construction projects. Future research should explore labor union impacts, cultural influences, client involvement, regulatory compliance, and risk mitigation strategies to further enhance construction quality management

Advancements in Flood Modeling: Techniques, Impacts, and Future Challenges

2025-05-02T07:54:56+00:00

Flooding is one of the most devastating natural disasters, causing significant loss of life, economic damage, and environmental destruction. With the increasing frequency and intensity of floods, largely driven by climate change, flood modeling has become a crucial tool for disaster preparedness, risk mitigation, and urban planning. This review explores the various types of flood models, including hydrological, hydraulic, and integrated models, highlighting their applications and advancements. The paper also discusses the integration of remote sensing, real-time forecasting, and AI to improve model accuracy and timeliness. Furthermore, it examines the far-reaching impacts of flood modeling on urban planning, environmental management, economic loss assessment, and climate adaptation. While the technology has advanced, challenges such as data limitations, computational complexity, and uncertainties in model predictions remain. The review concludes by emphasizing the need for continued refinement of flood models to enhance resilience and reduce the impacts of future flooding events.

Ensuring Reliability and Safety in Construction: A Review of Industry Challenges and Solutions

2025-05-02T08:34:40+00:00

The construction industry, while essential to global economic development, is frequently faced with significant reliability and safety challenges. Issues such as design and planning failures, material quality inconsistencies, labor shortages, and unforeseen site conditions often jeopardize the timely completion and quality of projects. Additionally, the sector is plagued by safety hazards, including falls, electrical accidents, and exposure to toxic materials, which contribute to a high rate of workplace injuries and fatalities. This review explores the key reliability and safety challenges in the construction industry, identifying critical factors that impact project outcomes and worker well-being. Furthermore, it highlights innovative solutions and strategies, such as Building Information Modeling (BIM), wearable technology, safety management systems, and robotic automation, which are being increasingly adopted to mitigate risks. Through a comprehensive examination of these factors, the article provides insights into the current state of safety and reliability in construction, offering recommendations for improving practices and fostering a safer, more reliable construction environment.

The Evolution and Future of Highways and Transportation Engineering

2025-05-02T08:44:32+00:00

Highways and transportation engineering have evolved significantly, shaping the modern infrastructure that supports the global movement of people and goods. This review examines the historical development, technological advancements, current challenges, and emerging trends within the field. From early road networks of ancient civilizations to the modern era of intelligent transportation systems (ITS) and autonomous vehicles, the advancements have revolutionized traffic management, safety, and sustainability. While challenges such as congestion, aging infrastructure, and environmental impact persist, new technologies like electric vehicles, smart roads, and connected transportation networks offer innovative solutions. Looking ahead, the future of highways and transportation engineering is poised to embrace sustainable, integrated, and efficient systems, enhancing mobility and reducing environmental footprints. This article provides an overview of the past, present, and future of highway and transportation engineering, offering insights into the transformative role of technology in shaping transportation infrastructure.

Strategic Engineering Asset Management: A Review of Best Practices and Future Directions

2025-05-02T08:55:08+00:00

Strategic Engineering Asset Management (SEAM) is a comprehensive approach to optimizing the lifecycle, performance, and value of assets within engineering-driven industries. As organizations face increasing pressures to improve operational efficiency, reduce costs, and minimize risks, effective asset management has become essential to achieving long-term success. This article explores the significance of SEAM, highlighting its key components, including asset lifecycle management, predictive maintenance, and data-driven decision-making. It also discusses best practices such as adopting a holistic approach, utilizing advanced technologies, and aligning asset management strategies with broader organizational goals. Additionally, challenges in implementing SEAM, such as data integration and budget constraints, are addressed. The article concludes by examining future trends in SEAM, including the use of artificial intelligence, digital twins, and blockchain, emphasizing the ongoing evolution of asset management practices in the face of technological advancements.